输入受限四旋翼飞行器的模糊自适应动态面轨迹跟踪控制

针对输入受限条件下四旋翼飞行器的轨迹跟踪控制问题,考虑系统存在模型动态不确定和未知外界干扰的情况,提出一种模糊自适应动态面轨迹跟踪控制方法.该方法设计干扰观测器估计位置模型中复合扰动项,利用模糊系统逼近姿态模型中不确定项和外界干扰,并引入双曲正切函数和辅助系统处理输入受限问题,结合反演法和动态面技术设计轨迹跟踪控制器,以降低控制算法的复杂性,最后选取李雅普诺夫函数证明闭环系统所有信号一致最终有界.应用大疆M100飞行器模型进行仿真验证,结果表明所设计的控制器能够有效处理模型动态不确定和未知外界干扰问题,避免飞行器工作过程中因输入饱和导致执行器失效现象,精确地完成轨迹跟踪控制任务.     

基于自适应模糊滑模控制的船舶航向控制器设计

针对船舶运动系统中固有的非线性、模型不确定性和风、浪、流等的干扰.提出了自适应模糊滑模控制(AFSMC)策略解决船舶的航向控制问题.通过采用模糊逻辑系统逼近系统未知函数,将滑模控制技术与自适应模糊控制技术相结合,设计了船舶航向AFSMC控制器.在滑模边界层内应用PI (proportional-integral)控制代替滑模控制中的切换项,削弱了滑模控制带来的抖振现象.借助李亚普诺夫函数证明了船舶运动系统中的信号都一致有界并利用Barbalat引理证明了跟踪误差渐近收敛到零.在参数摄动和外界干扰情况下进行了航向保持与改变仿真试验,采用AFSMC控制器得到了与无摄动和无干扰情况下相似的输出响应.实验结果表明,所提控制器能有效地处理系统不确定性和外界干扰,控制性能良好,具有很强的鲁棒性.     

Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。     

非对称饱和扇形输入非线性系统的自适应模糊控制

针对一类具有未知不确定性,且状态不可测的非线性系统,考虑了输入端的饱和非对称扇区非线性特性影响,提出了系统模型未知情形下基于自适应模糊观测器的跟踪控制方案,采用Lyapunov-Krasovskii函数给出了滑模控制器参数和模糊逻辑的自适应调整律.所提方法不仅可保证闭环跟踪系统的稳定性,还削弱了传统方法对模型结构的依赖...     

Hybrid Adaptive Type‐2 Fuzzy Tracking Control of Chaotic Oscillation Damping of Power Systems

In this paper a novel hybrid direct/indirect adaptive fuzzy neural network (FNN) moving sliding mode tracking controller for chaotic oscillation damping of power systems is developed. The proposed approach is established by providing a tradeoff between the indirect and direct FNN controllers. It is equipped with a novel moving sliding surface (MSS) to enhance the robustness of the controller against the present system uncertainties and unknown disturbances. The major contribution of the paper arises from the new simple tuning idea of the sliding surface slope and intercept of the MSS. This study is novel because the approach adopted tunes the sliding surface slope and intercept of MSS using two simple rules simultaneously. One advantage of the proposed approach is that the restriction of knowing the bounds of uncertainties is also removed due to the adaptive mechanism. Moreover, the stability of the control system is also presented. The proposed controller structure is successfully employed to damp the complicated chaotic oscillations of an interconnected power system, when such oscillations can be made by load perturbation of a power system working on its stability edges. Comparative simulation results are presented, which confirm that the proposed hybrid adaptive type‐2 fuzzy tracking controller shows superior tracking performance.     

Direct fuzzy model‐reference adaptive control

Intelligent systems may be viewed as a framework for solving the problems of nonlinear system control. The intelligence of the system in the nonlinear or changing environment is used to recognize in which environment the system currently resides and to service it appropriately. This paper presents a general methodology of adaptive control based on multiple models in fuzzy form to deal with plants with unknown parameters which depend on known plant variables. We introduce a novel model‐reference fuzzy adaptive control system which is based on the fuzzy basis function expansion. The generality of the proposed algorithm is substantiated by the Stone‐Weierstrass theorem which indicates that any continuous function can be approximated by fuzzy basis function expansion. In the sense of adaptive control this implies the adaptive law with fuzzified adaptive parameters which are obtained using Lyapunov stability criterion. The combination of adaptive control theory based on models obtained by fuzzy basis function expansion results in fuzzy direct model‐reference adaptive control which provides higher adaptation ability than basic adaptive‐control systems. The proposed control algorithm is the extension of direct model‐reference fuzzy adaptive‐control to nonlinear plants. The direct fuzzy adaptive controller directly adjusts the parameter of the fuzzy controller to achieve approximate asymptotic tracking of the model‐reference input. The main advantage of the proposed approach is simplicity together with high performance, and it has been shown that the closed‐loop system using the direct fuzzy adaptive controller is globally stable and the tracking error converges to the residual set which depends on fuzzification properties. The proposed approach can be implemented on a wide range of industrial processes. In the paper the foundation of the proposed algorithm are given and some simulation examples are shown and discussed. © 2002 Wiley Periodicals, Inc.     

An indirect model reference robust fuzzy adaptive control for a class of SISO nonlinear systems

In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties. The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters are computed at each time. The plant state tracks asymptotically the state of the reference model for any bounded reference input signal. Inverted pendulum and mass spring damper are used to check the performance of the proposed controller.     

An Adaptive Control Using Fuzzy Basis Function Expansions for a Class of Nonlinear Systems

An adaptive control using fuzzy basis function expansions is proposed for a class of nonlinear systems in this paper. It is shown that two system uncertainty bounds are approximated in a compact set by using fuzzy basis function expansion networks in the Lyapunov sense, and the outputs of the fuzzy networks are then used as the parameters of the controller to adaptively compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to unknown system dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can be guaranteed to asymptotically converge to zero. Simulation results are provided to demonstrate the effectiveness, simplicity and practicality of the proposed control scheme.     

含有不灵敏区有界不确定非线性系统的鲁棒跟踪控制

针对具有控制输入不灵敏区及有界不确定性的非线性系统,研究其鲁棒跟踪问题.利用变结构控制方法和自适应参数估计方法,在同时存在的参数、结构及干扰的不确定性和未知控制输入不灵敏区的情形下,提出了鲁棒控制律设计方法,并提出克服控制信号抖动的改进算法.所提出的控制律可以保证闭环系统的一致终结有界,并且算法比较简单,便于实现.用数字仿真方法验证了所得控制律设计方法的有效性.     

考虑齿隙伺服系统的反步自适应模糊控制

针对具有未知参数和齿隙非线性的机电伺服系统,引入一种近似死区函数建立了系统的数学模型,给出了死区函数中参数的选取方法.用两个自适应模糊逻辑系统在线逼近机电伺服系统中的未知参数和非线性环节,从而避免了对每个未知参数推导自适应律.基于反步法设计了自适应模糊控制器,可抑制未知参数和齿隙非线性对系统性能的影响.采用Lyapunov方法证明了位置跟踪误差的指数收敛性.与PID控制方法对比的仿真实验表明,本文方法能够显著减小齿轮间传递力矩的振荡,并具有很好的控制精度和鲁棒性.     

Adaptive fuzzy robust tracking controller design via small gain approach and its application

An adaptive fuzzy robust tracking control (AFRTC) algorithm is proposed for a class of nonlinear systems with the uncertain system function and uncertain gain function, which are all the unstructured (or nonrepeatable) state-dependent unknown nonlinear functions arising from modeling errors and external disturbances. The Takagi-Sugeno type fuzzy logic systems are used to approximate unknown uncertain functions and the AFRTC algorithm is designed by use of the input-to-state stability approach and small gain theorem. The algorithm is highlighted by three advantages: 1) the uniform ultimate boundedness of the closed-loop adaptive systems in the presence of nonrepeatable uncertainties can be guaranteed; 2) the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed; and 3) the adaptive mechanism with minimal learning parameterizations can be obtained. The performance and limitations of the proposed method are discussed. The uses of the AFRTC for the tracking control design of a pole-balancing robot system and a ship autopilot system to maintain the ship on a predetermined heading are demonstrated through two numerical examples. Simulation results show the effectiveness of the control scheme.     

非线性系统基于HM模型的自适应鲁棒跟踪控制

研究了非线性系统的跟踪控制问题，基于HM模型对非线性系统进行描述，并将全局模糊模型表示成不确定系统形式。在满足匹配条件下，针对未知不确定界，采用自适应鲁棒控制器，利用自适应变量信息来补偿系统的不确定性信息，实现了非线性系统的渐近跟踪控制。一级倒立摆仿真实验，验证了方案的有效性。控制器结构简单，规则少，具有应用价值。     

A robust adaptive fuzzy variable structure tracking control for the wheeled mobile robot: Simulation and experimental results

In this paper an adaptive fuzzy variable structure control (kinematic control) integrated with a proportional plus derivative control (dynamic control) is proposed as a robust solution to the trajectory tracking control problem for a differential wheeled mobile robot. The variable structure controller, based on the sliding mode theory, is a well known, proven control method, fit to deal with uncertainties and disturbances (e.g., structural and parameter uncertainties, external disturbances and operating limitations). To minimize the problems found in practical implementations of the classical variable structure controllers, an adaptive fuzzy logic controller replaces the discontinuous portion of the control signals (avoiding the chattering), causing the loss of invariance, but still ensuring the robustness to uncertainties and disturbances without having any a priori knowledge of their boundaries. Moreover, the adaptive fuzzy logic controller is a feasible tool to approximate any real continuous nonlinear system to arbitrary accuracy, and has a simple structure by using triangular membership functions, a low number of rules that must be evaluated, resulting in a lower computational load for execution, making it feasible for real time implementation. Stability analysis and the convergence of tracking errors as well as the adaptation laws are guaranteed with basis on the Lyapunov theory. Simulation and experimental results are explored to show the verification and validation of the proposed control strategy.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

基于终端滑模机器人模糊自适应路径跟踪控制

对质心位置未知的移动机器人系统设计了基于快速终端滑模的模糊自适应路径跟踪控制方法。该方法采用模糊逻辑系统逼近控制器中的未知函数,基于李亚普诺夫稳定性分析方法对未知参数设计自适应律,并设计鲁棒控制器来补偿逼近误差。该方法不但可以保证闭环系统中的所有信号有界,而且可使跟踪误差在有限时间内收敛到原点的小邻域内。仿真结果验证了方法的有效性。     

Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems

This work presents an adaptive fuzzy sliding mode controller (AFSMC) that combines a robust proportional integral control law for use in designing single-input single-output (SISO) nonlinear systems with uncertainties and external disturbances. The fuzzy logic system is used to approximate the unknown system function and the AFSMC algorithm is designed by used of sliding mode control techniques. Based on the Lyapunov theory, the proportional integral control law is designed to eliminate the chattering action of the control signal. The simplicity of the proposed scheme facilitates its implementation and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the signals involved are uniformly bounded. Simulation studies have shown that the proposed controller shows superior tracking performance.     

Adaptive fuzzy robust control of PMSM with smooth inverse based dead-zone compensation

It is a challenging work to design high precision/high performance motion controller for permanent magnet synchronous motor (PMSM) due to some difficulties, such as varying operating conditions, parametric uncertainties and external disturbances. In order to improve tracking control performance of PMSM, this paper proposes an adaptive fuzzy robust control (AFRC) algorithm with smooth inverse based dead-zone compensation. Instead of nonsmooth dead-zone inverse which would cause the possible control signal chattering phenomenon, a new smooth dead-zone inverse is proposed for non-symmetric dead-zone compensation in PMSM system. AFRC controller is synthesized by combining backstepping technique and small gain theorem. Discontinuous projectionbased parameter adaptive law is used to estimate unknown system parameters. The Takagi-Sugeno fuzzy logic systems are employed to approximate the unstructured dynamics. Robust control law ensures the robustness of closed loop control system. The proposed AFRC algorithm with smooth inverse based dead-zone compensation is verified on a practical PMSM control system. The comparative experimental results indicate that the smooth inverse for non-symmetric dead-zone nonlinearity can effectively avoid the chattering phenomenon which would be caused by nonsmooth dead-zone inverse, and the proposed control strategy can improve the PMSM output tracking performance.     

基于自适应滑模的欠驱动无人艇轨迹跟踪控制算法

针对欠驱动水面无人艇在航行过程中存在的海洋环境干扰、数学模型参数不确定、执行器故障等问题,提出了一种基于扰动观测器与神经网络技术的自适应滑模轨迹跟踪策略。在无人艇三自由度模型的基础上,结合视线制导率,提出了一种新的轨迹跟踪制导策略。采用自适应滑模控制技术设计了欠驱动无人艇轨迹跟踪控制器,有效地抑制了执行器衰减故障对无人艇控制系统的影响;同时运用了非线性扰动观测器和自适应径向基函数神经网络分别对无人艇受到的外界干扰和模型参数不确定性进行补偿和拟合,提高了控制系统的抗干扰能力。基于Lyapunov定理证明了所设计的控制系统的稳定性,并在MATLAB中进行了仿真测试。仿真结果表明,所提出的轨迹跟踪控制算法可以在较为复杂的环境下实现对欠驱动无人艇的精准控制;相较于对比算法,位置的平均跟踪误差减小了80%以上,具备较高的稳定性和鲁棒性。     

Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems

A fuzzy logic controller equipped with a training algorithm is developed such that the H^∞ tracking performance should be satisfied for a model-free nonlinear multiple-input multiple-output (MIMO) system, with external disturbances. Due to universal approximation theorem, fuzzy control provides nonlinear controller, i.e., fuzzy logic controllers, to perform the unknown nonlinear control actions and the tracking error, because of the matching error and external disturbance is attenuated to arbitrary desired level by using H^∞ tracking design technique. In this paper, a new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances. Therefore, linguistic fuzzy control rules can be directly incorporated into the controller and combine the H^∞ attenuation technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbance, data uncertainties, very well, but the adaptive type-1 fuzzy controller must spend more control effort in order to deal with noisy training data. Furthermore, the adaptive interval type-2 fuzzy controller can perform successful control and guarantee the global stability of the resulting closed-loop system and the tracking performance can be achieved.